Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. Method for reproducing visual or audio data synchronously by a group of devices, the method comprising: determining a spatial location of each of the devices of the group, partitioning the visual or audio content into content parts, each content part dedicated to be reproduced by one device of the group respectively based on the spatial location of the device, transmitting the dedicated content parts to their associated devices together with a time stamp, and reproducing the transmitted portions commonly and synchronously by the group of devices, wherein a distribution of the devices of the group is analyzed and the partitioning of the visual content is mapped to the distribution such that at least one feature of the visual content to be reproduced is optimized, and wherein the feature of the visual content is computed based on histograms or cluster model of one or more of local structure frequency, brightness, luminance or colors of the visual content and the optimization is performed towards cluster or histogram similarity.
2. The method according to claim 1 , wherein the step of partitioning the visual or audio content is performed repeatedly whenever a device joins or leaves the group.
This invention relates to dynamic partitioning of visual or audio content in a group communication system, addressing the challenge of efficiently distributing content among devices with varying capabilities or network conditions. The method involves partitioning the content into segments and assigning these segments to different devices in the group. When a new device joins or an existing device leaves the group, the content is repartitioned to optimize distribution. This ensures that all devices receive the content without overloading any single device or causing delays. The partitioning may be based on factors such as device processing power, network bandwidth, or user preferences. The system dynamically adjusts the distribution to maintain efficient and balanced content delivery as the group composition changes. This approach improves scalability and reliability in group communication environments where devices frequently join or leave.
3. The method according to claim 1 , wherein the visual or audio content is one of the following: an image, a video, an audio recording, a 3D object file or a virtual 3D space.
This invention relates to a method for processing and analyzing visual or audio content, including images, videos, audio recordings, 3D object files, and virtual 3D spaces. The method addresses the challenge of efficiently extracting and interpreting data from diverse multimedia formats to enable applications such as content recognition, object detection, or spatial analysis. The core process involves capturing or receiving the content, preprocessing it to enhance quality or extract features, and then applying machine learning or computer vision techniques to analyze the data. For images and videos, this may include object detection, facial recognition, or scene segmentation. For audio recordings, the method may involve speech recognition, sound classification, or noise reduction. In the case of 3D object files or virtual 3D spaces, the method may focus on spatial mapping, object interaction, or environmental modeling. The analysis results can be used for tasks such as content tagging, automated annotation, or real-time feedback in virtual environments. The method ensures compatibility with various input formats while maintaining accuracy and efficiency in processing.
4. The method according to claim 1 , wherein the determination of the spatial location of each of the devices of the group is performed based on images shot by built-in cameras of the devices.
This invention relates to a system for determining the spatial locations of multiple devices within a group, particularly in scenarios where precise positioning is needed for applications such as augmented reality, robotics, or environmental mapping. The problem addressed is the challenge of accurately locating devices in a shared space without relying on external infrastructure like GPS or pre-installed beacons, which may be unavailable or impractical in certain environments. The method involves using built-in cameras on each device to capture images of the surrounding area. These images are analyzed to identify common visual features or landmarks visible to multiple devices. By comparing these features across devices, the system calculates relative positions, enabling each device to determine its spatial location within the group. This approach leverages computer vision techniques to establish a shared coordinate system, allowing devices to collaborate in tasks requiring spatial awareness, such as navigation, object tracking, or cooperative robotics. The system may also incorporate additional data, such as device orientation or movement patterns, to refine location estimates. This ensures robustness in dynamic environments where visual features may change or become obscured. The method is particularly useful in indoor or GPS-denied areas where traditional positioning systems fail, providing a self-contained solution for device localization.
5. The method according to claim 4 , wherein the images shot by the built-in cameras of the devices include one or more markers a location of which is known, and that the determination of the spatial location is performed based on the markers in the images.
This invention relates to a method for determining the spatial location of devices equipped with built-in cameras. The problem addressed is accurately positioning devices in a physical space, particularly when GPS or other global positioning systems are unavailable or unreliable, such as indoors or in dense urban environments. The method involves capturing images using the built-in cameras of the devices. These images contain one or more markers whose locations are known in advance. The markers may be visual landmarks, such as QR codes, barcodes, or other identifiable patterns, placed at fixed positions in the environment. By analyzing the captured images, the system identifies these markers and uses their known positions to calculate the spatial location of the device. This process may involve triangulation, pattern recognition, or other geometric techniques to determine the device's position relative to the markers. The method may also include additional steps, such as calibrating the cameras to account for lens distortion or other optical imperfections, and refining the position estimate using multiple markers or sequential images. The system may further incorporate sensor data, such as accelerometers or gyroscopes, to improve accuracy over time. The determined spatial location can then be used for navigation, asset tracking, augmented reality, or other applications requiring precise positioning.
6. The method according to claim 4 , wherein each device shooting images for position determination transmits these images to a central processor, the central processor computes the spatial locations of the devices and sends back location information to the devices.
This invention relates to a system for determining the spatial locations of multiple devices using image-based positioning. The problem addressed is the need for accurate and efficient localization of devices in a shared environment, such as in industrial, robotic, or augmented reality applications, where precise positioning is critical for coordination and interaction. The system involves multiple devices equipped with imaging capabilities, such as cameras or sensors, that capture images of their surroundings. These devices transmit the captured images to a central processor, which processes the images to compute the spatial locations of the devices. The central processor analyzes the images to determine relative positions, possibly using techniques like feature matching, triangulation, or other computer vision algorithms. Once the positions are calculated, the central processor sends the location information back to the individual devices. This allows each device to know its precise position relative to others, enabling coordinated movement, interaction, or task execution. The system may also include additional features, such as real-time updates, error correction, or integration with other positioning systems (e.g., GPS or inertial sensors) to enhance accuracy. The central processor may further optimize the positioning process by adjusting parameters based on environmental conditions or device configurations. This approach ensures that devices can operate effectively in dynamic environments where precise spatial awareness is essential.
7. The method according to claim 4 , wherein after an initial determination of the spatial location is made, an update or refinement of the spatial location is performed based on data recorded by an inertial measurement unit (IMU) of the device.
This invention relates to spatial location tracking systems, particularly for devices equipped with inertial measurement units (IMUs). The problem addressed is the need for accurate and refined spatial location determination, especially in dynamic environments where initial positioning may be imprecise or subject to drift. The method involves first determining an initial spatial location of a device using any suitable positioning technique, such as GPS, Wi-Fi triangulation, or other sensor-based methods. Once this initial position is established, the system continuously refines or updates the spatial location by incorporating data from the device's IMU. The IMU provides measurements of acceleration, angular velocity, and sometimes magnetic field data, which are used to track the device's movement and orientation over time. By integrating these inertial measurements with the initial position, the system corrects for errors and improves positional accuracy, particularly in scenarios where external positioning signals may be weak or unavailable. The refinement process may involve sensor fusion techniques, where IMU data is combined with other sensor inputs (e.g., from cameras, LiDAR, or additional positioning systems) to enhance robustness. This approach is particularly useful for applications like augmented reality, autonomous navigation, and indoor positioning, where precise and real-time location tracking is critical. The method ensures that the device maintains accurate spatial awareness even when external positioning references are limited or unreliable.
8. The method according to claim 4 , wherein the determination of spatial locations of the devices based on images taken by the built-in cameras of the devices is based on computation of an essential matrix or fundamental matrix by applying at least one of an eight-point algorithm, a five-point algorithm, RANSAC, bundle adjustment and combinations thereof.
This invention relates to a method for determining the spatial locations of devices equipped with built-in cameras. The problem addressed is accurately estimating the positions of such devices in a shared environment, which is crucial for applications like augmented reality, robotics, and collaborative systems. The method involves capturing images from the cameras of the devices and analyzing these images to compute spatial relationships. The core of the method lies in calculating an essential matrix or fundamental matrix, which describes the geometric relationship between the devices' camera views. This computation is performed using one or more algorithms, including an eight-point algorithm, a five-point algorithm, RANSAC (Random Sample Consensus), bundle adjustment, or combinations thereof. These algorithms are well-known in computer vision for estimating camera motion and structure from multiple images. The essential matrix is derived from corresponding points identified in the images, allowing the determination of relative camera poses. The fundamental matrix, on the other hand, is used when the cameras' intrinsic parameters are unknown or not calibrated. By applying these techniques, the method enables precise localization of the devices in 3D space, even in dynamic or unstructured environments. This is particularly useful for applications requiring real-time positioning, such as multi-device coordination or augmented reality overlays. The use of robust algorithms like RANSAC ensures accuracy even in the presence of noise or outliers in the image data.
9. The method according to claim 4 , wherein the determination of spatial locations of the devices based on images taken by the built-in cameras of the devices is based on computation of structure-from-motion algorithms or multi-view geometry algorithms.
This invention relates to a system for determining the spatial locations of devices equipped with built-in cameras. The problem addressed is accurately locating devices in a three-dimensional space using visual data captured by their cameras, which is particularly useful in applications like augmented reality, robotics, or autonomous navigation. The method involves using images taken by the built-in cameras of the devices to compute their spatial positions. The core innovation lies in applying structure-from-motion (SfM) or multi-view geometry algorithms to analyze the images. Structure-from-motion algorithms reconstruct three-dimensional structures from two-dimensional image sequences by tracking feature points across multiple images and estimating camera motion. Multi-view geometry algorithms extend this by analyzing multiple images from different viewpoints to determine relative positions and orientations of the cameras. The system leverages these algorithms to process the visual data, enabling precise spatial localization of the devices. This approach eliminates the need for external positioning systems, such as GPS or dedicated sensors, by relying solely on the devices' built-in cameras and computational techniques. The method is scalable and adaptable to various environments, making it suitable for dynamic or GPS-denied scenarios. The invention enhances accuracy and robustness in device localization, improving applications that depend on spatial awareness.
10. The method according to claim 1 , wherein the determination of the spatial location is performed based on beacons emitted by beacon nodes whose location is known.
A system and method for determining the spatial location of a device or object within a defined area. The method involves using beacon nodes with known locations to emit signals, which are detected by the device or object to estimate its position. The beacon nodes may be distributed throughout the area to provide accurate and reliable positioning data. The system may employ various techniques, such as trilateration or triangulation, to calculate the device's position based on signal strength, time of arrival, or other measurable parameters. The method may also include filtering or processing the received signals to improve accuracy, such as by reducing noise or accounting for environmental factors. The system may be used in applications such as indoor navigation, asset tracking, or location-based services, where GPS or other global positioning systems are unavailable or unreliable. The method may further include dynamically adjusting the beacon node configuration or signal transmission parameters to optimize performance under varying conditions. The system may also integrate with other positioning technologies, such as Wi-Fi, Bluetooth, or ultra-wideband (UWB), to enhance accuracy or coverage. The method may be implemented in software, hardware, or a combination thereof, and may be deployed in various environments, including industrial, commercial, or residential settings.
11. The method according to claim 1 , wherein one dedicated device records at least one image by its built-in camera and registers a local coordinate system and that the at least one image is shared to the other devices of the group which themselves record images taken by their own built-in cameras, wherein each of the other devices computes its spatial position based on the received images, the received local coordinate system and the own recorded images and transmits its spatial location to the dedicated device.
This invention relates to a collaborative system for determining spatial positions of multiple devices using built-in cameras. The problem addressed is the need for accurate spatial positioning in environments where devices lack access to external positioning systems like GPS. The solution involves a dedicated device that captures at least one image using its built-in camera and establishes a local coordinate system. This image and coordinate system are shared with other devices in the group. Each of these devices also captures images using their own built-in cameras. Using the received image, the local coordinate system, and their own recorded images, each device computes its spatial position relative to the dedicated device. The computed spatial locations are then transmitted back to the dedicated device. This method enables precise positioning of multiple devices within a shared coordinate system without relying on external infrastructure, making it suitable for indoor or GPS-denied environments. The system leverages image-based localization techniques to enhance accuracy and coordination among devices.
12. The method according to claim 1 , wherein the feature of the visual content is computed based on at least one of a local structure frequency, brightness, luminance and colors of the visual content.
This invention relates to methods for analyzing visual content, particularly for extracting and computing features from images or video frames to enhance processing, recognition, or compression. The core problem addressed is the need for efficient and accurate feature extraction from visual data to improve tasks such as object detection, image classification, or video encoding. The method involves computing features of visual content by analyzing at least one of the following characteristics: local structure frequency, brightness, luminance, and colors. Local structure frequency refers to patterns or textures within small regions of the image, which can help distinguish between different objects or surfaces. Brightness and luminance provide information about the overall light intensity and distribution, aiding in contrast and detail preservation. Colors are analyzed to capture chromatic information, which is crucial for distinguishing objects based on hue, saturation, and other color properties. By combining these features, the method enables more robust and discriminative analysis of visual content. This can be applied in various applications, such as improving image compression algorithms, enhancing object recognition in computer vision systems, or optimizing video encoding for streaming services. The approach ensures that the extracted features are both computationally efficient and highly informative, making it suitable for real-time processing in resource-constrained environments.
13. The method according to claim 1 , wherein the cluster or histogram similarity is calculated based on earth's mover distance or Euclidean distance.
A method for analyzing data clusters or histograms involves calculating similarity between them using either Earth Mover's Distance (EMD) or Euclidean distance. EMD measures the minimum cost of transforming one distribution into another, accounting for the spatial arrangement of data points, while Euclidean distance calculates the straight-line distance between points in a multi-dimensional space. This approach is useful in fields like image processing, pattern recognition, and data mining, where comparing distributions or clusters is essential. The method helps assess how closely related two sets of data are, improving accuracy in tasks such as object recognition, anomaly detection, or clustering analysis. By using EMD or Euclidean distance, the method provides a robust way to quantify similarity, even when dealing with complex or high-dimensional data. This technique is particularly valuable when traditional distance metrics fail to capture the underlying structure of the data.
14. The method according to claim 1 , wherein information on a direction towards the spatial location of the devices that would result in an improved cluster or histogram similarity is transmitted to the devices of the group.
This invention relates to optimizing the spatial arrangement of devices in a network to improve clustering or histogram similarity. The problem addressed is the need to dynamically adjust device positions to enhance data analysis accuracy, such as in sensor networks, IoT deployments, or distributed computing systems. The method involves analyzing the spatial distribution of devices and determining optimal directions for movement to improve clustering or histogram similarity metrics. Once the optimal directions are calculated, this directional information is transmitted to the devices, enabling them to adjust their positions accordingly. The method may also involve evaluating the effectiveness of the adjustments by comparing pre- and post-movement similarity metrics. The invention can be applied in scenarios where devices must self-organize to improve data collection, signal processing, or computational efficiency. The directional guidance ensures that devices move in a coordinated manner to achieve the desired clustering or histogram similarity, reducing manual intervention and improving system performance.
15. The method according to claim 1 , wherein a virtual display area is determined which is spanned by the devices of the group and the visual content is partitioned based on the location of the devices within the virtual display area and the size of the devices.
This invention relates to distributed display systems where multiple devices collaborate to form a larger virtual display area. The problem addressed is efficiently partitioning visual content across these devices to create a seamless viewing experience, accounting for their varying locations and sizes. The method involves determining a virtual display area that encompasses all devices in a group. The visual content is then divided into segments based on the spatial arrangement of the devices within this virtual area and their individual display sizes. This ensures that each device receives the appropriate portion of the content, maintaining proper alignment and scaling across the entire display. The partitioning process dynamically adjusts to changes in device positions or sizes, allowing for real-time reconfiguration of the display layout. The invention also includes techniques for synchronizing the rendering of content across devices to prevent visual artifacts and ensuring smooth transitions when devices are added or removed. By leveraging the collective display capabilities of multiple devices, this approach enables flexible, large-scale displays without requiring specialized hardware. The solution is particularly useful in collaborative environments, presentations, or interactive installations where multiple screens are used to extend a single visual workspace.
16. The method according to claim 1 , wherein a virtual display area is determined which is spanned by the devices of the group and the audio content is partitioned based on the location of the device within the virtual display area.
This invention relates to distributed audio playback systems where multiple devices collaborate to create a cohesive audio experience. The problem addressed is the lack of spatial coordination in multi-device audio setups, leading to inconsistent sound distribution and poor listener experience. The method involves a group of audio playback devices working together to form a virtual display area. This area is defined by the spatial arrangement of the devices, which may be determined using techniques like triangulation, signal strength analysis, or user input. Once the virtual display area is established, audio content is partitioned and assigned to individual devices based on their positions within this area. For example, devices located at the edges of the virtual display may receive audio signals intended for those spatial regions, while central devices handle the core audio content. This partitioning ensures that the audio is distributed in a way that aligns with the physical layout of the devices, creating a more immersive and spatially accurate sound experience. The method may also include dynamic adjustments to the virtual display area and audio partitioning as devices are added, removed, or moved, ensuring continuous optimization of the audio playback. The system can be applied in home theater setups, public address systems, or any scenario requiring coordinated multi-device audio playback.
17. The method according to claim 16 , wherein the audio content is music and each part of the audio content corresponds to one instrument.
This invention relates to audio processing, specifically a method for analyzing and manipulating audio content, particularly music, where the audio is divided into distinct parts corresponding to individual instruments. The method involves separating the audio into these instrument-specific parts, allowing for independent processing, modification, or extraction of each part. This enables tasks such as isolating a single instrument from a full mix, adjusting the volume or effects of specific instruments, or generating new arrangements by recombining the separated parts. The technique addresses the challenge of working with multi-instrument audio tracks where traditional methods may struggle to accurately isolate individual components. By leveraging advanced signal processing or machine learning techniques, the method ensures precise separation of each instrument's contribution, maintaining high audio quality. The separated parts can then be individually edited, mixed, or analyzed, providing greater flexibility in audio production, remixing, or content creation. This approach is particularly useful in music production, post-production, and applications requiring detailed audio manipulation.
18. The method according to claim 1 , wherein parts of the audio content are individually processed so that audio beamforming or wave field synthesis is achieved by the entirety of the devices of the group.
This invention relates to audio processing systems where multiple devices collaborate to enhance audio playback quality. The problem addressed is the need for coordinated audio processing across a group of devices to achieve advanced spatial audio effects, such as beamforming or wave field synthesis, without requiring centralized control or complex synchronization. The method involves distributing audio content processing tasks among the devices in a group. Each device independently processes specific parts of the audio content, such as different frequency bands, time segments, or spatial components. The processed audio signals are then combined across the devices to produce a coherent audio output. This distributed approach allows the group of devices to collectively generate directional sound beams or synthesize wave fields, improving sound localization and immersion. The method ensures that the individual processing steps performed by each device contribute to the overall spatial audio effect. For example, one device may handle low-frequency components while another processes high-frequency components, or devices may synchronize their outputs to create phase-coherent wavefronts. The system dynamically adjusts processing parameters based on device capabilities and environmental factors to optimize audio quality. This technique enables scalable, decentralized audio systems where multiple devices work together to achieve high-fidelity spatial audio without relying on a single master device. The approach is particularly useful in multi-speaker setups, smart home environments, or public address systems where coordinated audio playback is desired.
19. The method according to claim 1 , wherein a virtual display area that is spanned by the devices of the group is determined with corner points computed with a convex-hull algorithm.
This invention relates to a method for determining a virtual display area in a multi-device system, addressing the challenge of dynamically configuring display spaces across multiple devices to create a seamless visual workspace. The method involves grouping multiple devices, such as tablets, smartphones, or monitors, to form a cohesive display surface. A virtual display area is then defined by calculating its boundary using a convex-hull algorithm, which identifies the outermost corner points of the grouped devices. This ensures the virtual display area encompasses all devices in the group while maintaining a geometrically optimal shape. The convex-hull algorithm efficiently computes the boundary by connecting the farthest points of the devices, eliminating concavities and ensuring the display area is fully covered without gaps. The method may also include adjusting the virtual display area dynamically as devices are added or removed from the group, recalculating the convex hull to maintain an accurate boundary. This approach enables seamless multi-device collaboration, enhancing productivity and user experience in collaborative or extended display environments.
20. The method according to claim 1 , wherein, all of the content parts are transmitted to each of the devices of the group, wherein for each of the content parts information is added that allows identification of an associated device, and each device reproduces its dedicated content part.
This invention relates to a method for distributing and reproducing content parts across multiple devices in a group. The method addresses the challenge of synchronizing and managing content playback across multiple devices, ensuring that each device receives and reproduces only its designated content part while maintaining coordination with the other devices in the group. The method involves transmitting all content parts to each device in the group. For each content part, additional information is included to identify the associated device, allowing each device to determine which content part is intended for it. Upon receiving the content parts, each device reproduces only the content part that matches its identification information, while ignoring the other content parts. This ensures that each device plays back its dedicated content part in synchronization with the other devices in the group. The method may be used in applications such as multi-device audio or video playback systems, where different devices are responsible for different segments of the content. By distributing all content parts to each device but enabling selective reproduction, the method simplifies content management and ensures accurate synchronization without requiring complex coordination protocols. The approach reduces the need for centralized control and minimizes the risk of playback errors due to misalignment between devices.
21. The method according to claim 1 , wherein synchronization of reproduction is based on a network time protocol.
A method for synchronizing media reproduction across multiple devices in a networked environment addresses the challenge of maintaining precise timing alignment when streaming or playing back media content. The method involves coordinating playback timing among devices to ensure synchronized media delivery, which is critical for applications such as multi-device audio systems, distributed video playback, or collaborative media experiences. To achieve this, the method relies on a network time protocol (NTP) to synchronize the internal clocks of the devices, ensuring that each device references a common time source. This synchronization allows the devices to start and stop media playback at the same time, minimizing delays and drift. The method may also include compensating for network latency and processing delays to further refine synchronization accuracy. By using NTP, the system ensures that all devices operate within a tightly controlled time frame, reducing discrepancies in playback timing. This approach is particularly useful in environments where precise synchronization is required, such as in professional audio-visual setups, live streaming, or multi-device entertainment systems. The method may also incorporate additional synchronization techniques, such as periodic time adjustments or predictive algorithms, to maintain alignment over extended playback sessions.
22. System for reproducing visual or audio content synchronously, the system comprising a group of devices and a processor commonly used by the devices, wherein the devices and the processor are configured to execute the method steps of claim 1 with the processor being configured to execute the partitioning.
This system enables synchronized playback of visual or audio content across multiple devices. The problem addressed is the difficulty of ensuring precise synchronization when multiple devices independently play the same content, leading to delays or desynchronization. The system includes a group of devices and a shared processor that coordinates playback timing. The processor partitions the content into segments and assigns these segments to the devices, ensuring each device receives the correct portion of the content for playback. The devices then play their assigned segments in a coordinated manner, with the processor managing timing to maintain synchronization. This approach reduces latency and ensures all devices play the content simultaneously, improving the user experience in applications like multi-device media playback, live events, or distributed audio-visual systems. The system dynamically adjusts partitioning and timing to account for network delays or device processing variations, maintaining synchronization even in varying conditions.
23. The system according to claim 22 , wherein the commonly used processor is included in one of the devices of the group.
A system for distributed computing involves multiple devices sharing a commonly used processor to execute tasks. The processor is integrated into one of the devices within a group of interconnected devices. This setup allows the processor to be utilized by other devices in the group, improving computational efficiency and resource sharing. The system may include mechanisms for task allocation, load balancing, and communication between devices to ensure optimal use of the shared processor. By centralizing processing power in one device, the system reduces redundancy and enhances overall performance. The shared processor can handle tasks from multiple devices, enabling more efficient resource utilization and reducing the need for each device to have its own dedicated processor. This approach is particularly useful in environments where devices have limited processing capabilities or where energy efficiency is a priority. The system may also include features for managing conflicts, prioritizing tasks, and ensuring secure communication between devices. The shared processor can be dynamically allocated based on demand, allowing the system to adapt to varying workloads and optimize performance. This configuration is beneficial in applications such as IoT networks, edge computing, and distributed sensor systems where efficient resource management is critical.
Unknown
October 20, 2020
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.